The performance of a machine learning algorithm improves with experience regarding to the task [1]} .
The emergence of large-scale data sets of materials, such as the Inorganic Crystal Structure Database (ICSD) [2]}, the Open Quantum Materials Database (OQMD) [3]}, and the Material Project (MP) [4]}, greatly empowers the applications of novel machine learning technologies in the field of materials science.
For materials science, an algorithm typically addresses a given problem by optimizing model parameters based on existing material samples that have been experimentally validated.
The rich information contained in large-scale data sets makes the complex model feasible with minimal human interference.
